Ignite Creation Date:
2025-12-26 @ 4:01 PM
Ignite Modification Date:
2025-12-31 @ 12:44 PM
Study NCT ID:
NCT03517306
Status:
UNKNOWN
Last Update Posted:
2019-04-04
First Post:
2018-04-19
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
PET/CT Based Radiomics for Lung Cancer (PERL)
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D008175', 'term': 'Lung Neoplasms'}], 'ancestors': [{'id': 'D012142', 'term': 'Respiratory Tract Neoplasms'}, {'id': 'D013899', 'term': 'Thoracic Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2018-05-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2019-04', 'completionDateStruct': {'date': '2019-09-28', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2019-04-02', 'studyFirstSubmitDate': '2018-04-19', 'studyFirstSubmitQcDate': '2018-05-03', 'lastUpdatePostDateStruct': {'date': '2019-04-04', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2018-05-07', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2019-05-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Creation of a FDG PET/CT based radiomic score for survival', 'timeFrame': 'Time Frame: 3 years', 'description': 'Multiple quantitative radiomic features including SUV, metabolic volume, shape and texture will be measured from FDG PET/CT images. The all subjects will be randomly separated into a training and validation data. The multiple image features will be aggregated into a single combined radiomic score for survival with an appropriate machine learning method and the training data.'}], 'secondaryOutcomes': [{'measure': 'Validation of a FDG PET/CT based radiomic score for survival', 'timeFrame': 'Time Frame: 3 years', 'description': 'The created radiomic score developed in the primary outcome will be evaluated with the validation data in terms of survival(progress-free or overall survival)'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['lung cancer, FDG, PET/CT, radiomics'], 'conditions': ['Lung Cancer']}, 'descriptionModule': {'briefSummary': 'The investigators investigate the utility of FDG PET/CT based radiomics in lung cancer, including diagnosis and prognosis.', 'detailedDescription': 'Recent studies have shown that, in addition to inter-tumor heterogeneity, tumors often display startling intratumoral heterogeneity in various features including histology, gene expression, genotype, and metastatic and proliferative potential, which is often associated with adverse tumor biology. Unfortunately, it is difficult to assess intratumoral heterogeneity with random sampling or biopsy as this does not represent the full extent of phenotypic or genetic variation within a tumor. Given the limitations of current biopsy strategies, there is an important potential for medical imaging, which has the ability to capture intratumoral heterogeneity in a non-invasive way.\n\nBorrowed from the concept in genomics and/or proteomics, radiomics was specifically proposed for medical or radiological images. It is a promising technique for improving diagnosis, staging, prognosis, treatment response prediction and potentially allowing personalization of cancer treatment. It is a process of extraction and analysis of high-dimensional image features from radiological images obtained with CT, MR or PET, which could be either qualitative or quantitative. The basic assumption of radiomics is that tumor biology could be captured by radiomic features .\n\nThe purpose of this study is to investigate the utility of FDG PET/CT based radiomics in lung cancer. Four PET/CT centers will be involved in this study, in which more than 1000 patients diagnosed as lung cancer will be retrospectively enrolled.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'All patients diagnosed as lung cancer patients who had a FDG PET/CT scan before treatment between 1 Jan, 2013 and 30 December, 2016 in the four collaborative hospitals.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n\\- All patients diagnosed as lung cancer patients who had a FDG PET/CT scan before treatment between 1 Jan, 2013 and 30 December, 2016 in the four collaborative hospitals.\n\nExclusion Criteria:\n\n\\- The patient without follow-up information'}, 'identificationModule': {'nctId': 'NCT03517306', 'briefTitle': 'PET/CT Based Radiomics for Lung Cancer (PERL)', 'organization': {'class': 'OTHER', 'fullName': 'Second Affiliated Hospital of Wenzhou Medical University'}, 'officialTitle': 'PET/CTbased Radiomics for Lung Cancer (PERL): a Retrospective Multi-center Study', 'orgStudyIdInfo': {'id': 'SAHoWMU-CR2018-05-223'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Beijing', 'description': 'No interventions', 'interventionNames': ['Other: No Interventions']}, {'label': 'Ningxia', 'description': 'No interventions', 'interventionNames': ['Other: No Interventions']}, {'label': 'Wenzhou', 'description': 'No interventions', 'interventionNames': ['Other: No Interventions']}, {'label': 'Changzhou', 'description': 'No interventions', 'interventionNames': ['Other: No Interventions']}], 'interventions': [{'name': 'No Interventions', 'type': 'OTHER', 'description': 'No Interventions', 'armGroupLabels': ['Beijing', 'Changzhou', 'Ningxia', 'Wenzhou']}]}, 'contactsLocationsModule': {'locations': [{'zip': '325000', 'city': 'Wenzhou', 'state': 'Zhejiang', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Jianhua Yan, PhD', 'role': 'CONTACT', 'email': 'jianhua.yan@gmail.com', 'phone': '86-15824497979'}, {'name': 'Minfu Yang, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Juan Li, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Xiangwu Zheng, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Yuetao Wang, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Second Affiliated Hospital of Wenzhou Medical University', 'geoPoint': {'lat': 27.99942, 'lon': 120.66682}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'there is no plan to make individual participant data (IPD) available to other researchers.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Second Affiliated Hospital of Wenzhou Medical University', 'class': 'OTHER'}, 'collaborators': [{'name': 'Beijing Chao Yang Hospital', 'class': 'OTHER'}, {'name': 'General Hospital of Ningxia Medical University', 'class': 'OTHER'}, {'name': 'Soochow University', 'class': 'OTHER'}, {'name': 'First Affiliated Hospital of Wenzhou Medical University', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Jianhua Yan', 'investigatorAffiliation': 'Second Affiliated Hospital of Wenzhou Medical University'}}}}